{"id":"W2931889821","doi":"10.1016/s2468-2667(19)30041-6","title":"Screening interval: a public health blind spot","year":2019,"lang":"en","type":"letter","venue":"The Lancet Public Health","topic":"Health Promotion and Cardiovascular Prevention","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Blind spot; Medicine; Public health; Interval (graph theory); Optometry; Computer science; Nursing; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09576306,0.001471254,0.004511805,0.008558157,0.003474146,0.0153927,0.006051539,0.01923674,0.04924978],"category_scores_gemma":[0.3846773,0.001467172,0.002802784,0.00854389,0.009569937,0.02584601,0.01097746,0.02166177,0.009965671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005661497,"about_ca_system_score_gemma":0.01979059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005813614,"about_ca_topic_score_gemma":0.003495956,"domain_scores_codex":[0.9172065,0.04533216,0.008236177,0.006723714,0.01927443,0.003227059],"domain_scores_gemma":[0.6413495,0.255152,0.02083167,0.02909729,0.03983042,0.0137391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001322824,0.0001260583,0.002103418,0.004986056,0.0003612364,0.0001622494,0.0008748447,0.0001998652,0.0001634296,0.05509458,0.6357592,0.2988463],"study_design_scores_gemma":[0.001198698,0.0005562807,0.005888452,0.03067185,0.00111856,0.0004785,0.002025429,0.0008935782,0.000604565,0.1458464,0.8104399,0.0002776536],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.001107008,0.05910901,0.004443278,0.8643959,0.05852888,0.0001337814,0.002450855,0.0004361267,0.009395185],"genre_scores_gemma":[0.07484432,0.09425338,0.01688144,0.6575143,0.1358643,0.001128363,0.003862994,0.001069893,0.01458093],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.09576306,"threshold_uncertainty_score":0.5064495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2439851910288808,"score_gpt":0.3965275809615301,"score_spread":0.1525423899326493,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}